{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a664f981",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import csv\n",
    "\n",
    "data_path = Path.cwd().parent / \"data\"\n",
    "fips_csv_path = data_path / \"fips_states_2010.csv\"\n",
    "csv_path = data_path / \"score\" / \"csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7df430cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EJSCreen csv Load\n",
    "ejscreen_csv = data_path / \"dataset\" / \"ejscreen_2020\" / \"usa.csv\"\n",
    "df = pd.read_csv(ejscreen_csv, dtype={'ID': 'string'}, low_memory=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27677132",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# calculate percentiles\n",
    "df['lesshs_percentile'] = df.LESSHSPCT.rank(pct = True)\n",
    "df['lowin_percentile'] = df.LOWINCPCT.rank(pct = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f7b864f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate scores\n",
    "df['score_a'] = df[['lesshs_percentile', 'lowin_percentile']].mean(axis=1)\n",
    "df['score_b'] = df.lesshs_percentile * df.lowin_percentile\n",
    "\n",
    "# Create percentiles for the scores \n",
    "df['score_a_percentile'] = df.score_a.rank(pct = True)\n",
    "df['score_b_percentile'] = df.score_b.rank(pct = True)\n",
    "df['score_a_top_percentile_25'] = df['score_a_percentile'] >= 0.75\n",
    "df['score_b_top_percentile_25'] = df['score_b_percentile'] >= 0.75\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91755bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# strip calculations\n",
    "df = df[[\"ID\", \"score_a_percentile\", \"score_b_percentile\",\"score_a_top_percentile_25\",\"score_b_top_percentile_25\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3a65af4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write nationwide csv\n",
    "df.to_csv(csv_path / f\"usa.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58ddd8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write per state csvs\n",
    "with open(fips_csv_path) as csv_file:\n",
    "    csv_reader = csv.reader(csv_file, delimiter=\",\")\n",
    "    line_count = 0\n",
    "\n",
    "    for row in csv_reader:\n",
    "        if line_count == 0:\n",
    "            line_count += 1\n",
    "        else:\n",
    "            fips = row[0].strip()\n",
    "            print(f\"Generating data{fips} csv\")\n",
    "            df1 = df[df.ID.str[:2] == fips]\n",
    "            # we need to name the file data01.csv for ogr2ogr csv merge to work\n",
    "            df1.to_csv(csv_path / f\"data{fips}.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bce50823",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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